Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud

2021/07/03 by Dan Jia, Jia, Dan, Bastian Leibe +1 · 1 citation
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Robotics (cs.RO) #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.2107.06780

openalex publication_date 2021/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this preliminary work we attempt to apply submanifold sparse convolution to the task of 3D person detection. In particular, we present Person-MinkUNet, a single-stage 3D person detection network based on Minkowski Engine with U-Net architecture. The network achieves a 76.4% average precision (AP) on the JRDB 3D detection benchmark.

Citations

• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Cited by

• Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems

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